# Uncertainty Analysis

Compute parameter variability, plot confidence bounds

When you estimate the model parameters from data, you obtain
their nominal values that are accurate within a confidence region.
The size of this region is determined by the values of the parameter
uncertainties computed during estimation. The magnitude of the uncertainties
provide a measure of the reliability of the model. You can compute
and visualize the effect of parameter uncertainties on the model response
in time and frequency domains.

## Functions

present |
Display model information, including estimated uncertainty |

simsd |
Simulate linear models with uncertainty using Monte Carlo
method |

freqresp |
Frequency response over grid |

rsample |
Random sampling of linear identified systems |

showConfidence |
Display confidence regions on response plots for identified
models |

getcov |
Parameter covariance of linear identified parametric model |

setcov |
Set parameter covariance data in identified model |

translatecov |
Translate parameter covariance
across model operations |

step |
Step response plot of
dynamic system |

stepplot |
Plot step response and return plot handle |

impulse |
Impulse response plot
of dynamic system; impulse response data |

bode |
Bode plot of frequency response, magnitude and phase of
frequency response |

bodemag |
Bode magnitude response of LTI models |

nyquist |
Nyquist plot of
frequency response |

nyquistplot |
Nyquist plot with additional plot customization options |

iopzmap |
Plot pole-zero map for I/O pairs of model |

iopzplot |
Plot pole-zero map for I/O pairs and return plot handle |

tfdata |
Access
transfer function data |

zpkdata |
Access zero-pole-gain data |